1 | // This file is part of Eigen, a lightweight C++ template library
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2 | // for linear algebra.
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3 | //
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4 | // Copyright (C) 2008-2009 Guillaume Saupin <guillaume.saupin@cea.fr>
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5 | //
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6 | // This Source Code Form is subject to the terms of the Mozilla
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7 | // Public License v. 2.0. If a copy of the MPL was not distributed
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8 | // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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9 |
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10 | #ifndef EIGEN_SKYLINEMATRIX_H
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11 | #define EIGEN_SKYLINEMATRIX_H
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12 |
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13 | #include "SkylineStorage.h"
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14 | #include "SkylineMatrixBase.h"
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15 |
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16 | namespace Eigen {
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17 |
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18 | /** \ingroup Skyline_Module
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19 | *
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20 | * \class SkylineMatrix
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21 | *
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22 | * \brief The main skyline matrix class
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23 | *
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24 | * This class implements a skyline matrix using the very uncommon storage
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25 | * scheme.
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26 | *
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27 | * \param _Scalar the scalar type, i.e. the type of the coefficients
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28 | * \param _Options Union of bit flags controlling the storage scheme. Currently the only possibility
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29 | * is RowMajor. The default is 0 which means column-major.
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30 | *
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31 | *
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32 | */
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33 | namespace internal {
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34 | template<typename _Scalar, int _Options>
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35 | struct traits<SkylineMatrix<_Scalar, _Options> > {
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36 | typedef _Scalar Scalar;
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37 | typedef Sparse StorageKind;
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38 |
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39 | enum {
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40 | RowsAtCompileTime = Dynamic,
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41 | ColsAtCompileTime = Dynamic,
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42 | MaxRowsAtCompileTime = Dynamic,
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43 | MaxColsAtCompileTime = Dynamic,
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44 | Flags = SkylineBit | _Options,
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45 | CoeffReadCost = NumTraits<Scalar>::ReadCost,
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46 | };
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47 | };
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48 | }
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49 |
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50 | template<typename _Scalar, int _Options>
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51 | class SkylineMatrix
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52 | : public SkylineMatrixBase<SkylineMatrix<_Scalar, _Options> > {
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53 | public:
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54 | EIGEN_SKYLINE_GENERIC_PUBLIC_INTERFACE(SkylineMatrix)
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55 | EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(SkylineMatrix, +=)
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56 | EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(SkylineMatrix, -=)
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57 |
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58 | using Base::IsRowMajor;
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59 |
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60 | protected:
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61 |
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62 | typedef SkylineMatrix<Scalar, (Flags&~RowMajorBit) | (IsRowMajor ? RowMajorBit : 0) > TransposedSkylineMatrix;
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63 |
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64 | Index m_outerSize;
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65 | Index m_innerSize;
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66 |
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67 | public:
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68 | Index* m_colStartIndex;
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69 | Index* m_rowStartIndex;
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70 | SkylineStorage<Scalar> m_data;
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71 |
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72 | public:
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73 |
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74 | inline Index rows() const {
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75 | return IsRowMajor ? m_outerSize : m_innerSize;
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76 | }
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77 |
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78 | inline Index cols() const {
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79 | return IsRowMajor ? m_innerSize : m_outerSize;
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80 | }
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81 |
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82 | inline Index innerSize() const {
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83 | return m_innerSize;
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84 | }
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85 |
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86 | inline Index outerSize() const {
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87 | return m_outerSize;
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88 | }
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89 |
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90 | inline Index upperNonZeros() const {
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91 | return m_data.upperSize();
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92 | }
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93 |
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94 | inline Index lowerNonZeros() const {
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95 | return m_data.lowerSize();
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96 | }
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97 |
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98 | inline Index upperNonZeros(Index j) const {
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99 | return m_colStartIndex[j + 1] - m_colStartIndex[j];
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100 | }
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101 |
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102 | inline Index lowerNonZeros(Index j) const {
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103 | return m_rowStartIndex[j + 1] - m_rowStartIndex[j];
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104 | }
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105 |
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106 | inline const Scalar* _diagPtr() const {
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107 | return &m_data.diag(0);
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108 | }
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109 |
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110 | inline Scalar* _diagPtr() {
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111 | return &m_data.diag(0);
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112 | }
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113 |
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114 | inline const Scalar* _upperPtr() const {
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115 | return &m_data.upper(0);
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116 | }
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117 |
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118 | inline Scalar* _upperPtr() {
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119 | return &m_data.upper(0);
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120 | }
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121 |
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122 | inline const Scalar* _lowerPtr() const {
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123 | return &m_data.lower(0);
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124 | }
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125 |
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126 | inline Scalar* _lowerPtr() {
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127 | return &m_data.lower(0);
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128 | }
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129 |
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130 | inline const Index* _upperProfilePtr() const {
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131 | return &m_data.upperProfile(0);
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132 | }
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133 |
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134 | inline Index* _upperProfilePtr() {
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135 | return &m_data.upperProfile(0);
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136 | }
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137 |
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138 | inline const Index* _lowerProfilePtr() const {
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139 | return &m_data.lowerProfile(0);
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140 | }
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141 |
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142 | inline Index* _lowerProfilePtr() {
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143 | return &m_data.lowerProfile(0);
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144 | }
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145 |
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146 | inline Scalar coeff(Index row, Index col) const {
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147 | const Index outer = IsRowMajor ? row : col;
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148 | const Index inner = IsRowMajor ? col : row;
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149 |
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150 | eigen_assert(outer < outerSize());
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151 | eigen_assert(inner < innerSize());
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152 |
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153 | if (outer == inner)
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154 | return this->m_data.diag(outer);
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155 |
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156 | if (IsRowMajor) {
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157 | if (inner > outer) //upper matrix
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158 | {
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159 | const Index minOuterIndex = inner - m_data.upperProfile(inner);
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160 | if (outer >= minOuterIndex)
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161 | return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
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162 | else
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163 | return Scalar(0);
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164 | }
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165 | if (inner < outer) //lower matrix
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166 | {
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167 | const Index minInnerIndex = outer - m_data.lowerProfile(outer);
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168 | if (inner >= minInnerIndex)
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169 | return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
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170 | else
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171 | return Scalar(0);
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172 | }
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173 | return m_data.upper(m_colStartIndex[inner] + outer - inner);
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174 | } else {
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175 | if (outer > inner) //upper matrix
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176 | {
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177 | const Index maxOuterIndex = inner + m_data.upperProfile(inner);
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178 | if (outer <= maxOuterIndex)
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179 | return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
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180 | else
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181 | return Scalar(0);
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182 | }
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183 | if (outer < inner) //lower matrix
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184 | {
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185 | const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
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186 |
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187 | if (inner <= maxInnerIndex)
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188 | return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
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189 | else
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190 | return Scalar(0);
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191 | }
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192 | }
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193 | }
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194 |
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195 | inline Scalar& coeffRef(Index row, Index col) {
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196 | const Index outer = IsRowMajor ? row : col;
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197 | const Index inner = IsRowMajor ? col : row;
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198 |
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199 | eigen_assert(outer < outerSize());
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200 | eigen_assert(inner < innerSize());
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201 |
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202 | if (outer == inner)
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203 | return this->m_data.diag(outer);
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204 |
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205 | if (IsRowMajor) {
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206 | if (col > row) //upper matrix
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207 | {
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208 | const Index minOuterIndex = inner - m_data.upperProfile(inner);
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209 | eigen_assert(outer >= minOuterIndex && "you try to acces a coeff that do not exist in the storage");
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210 | return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
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211 | }
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212 | if (col < row) //lower matrix
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213 | {
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214 | const Index minInnerIndex = outer - m_data.lowerProfile(outer);
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215 | eigen_assert(inner >= minInnerIndex && "you try to acces a coeff that do not exist in the storage");
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216 | return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
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217 | }
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218 | } else {
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219 | if (outer > inner) //upper matrix
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220 | {
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221 | const Index maxOuterIndex = inner + m_data.upperProfile(inner);
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222 | eigen_assert(outer <= maxOuterIndex && "you try to acces a coeff that do not exist in the storage");
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223 | return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
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224 | }
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225 | if (outer < inner) //lower matrix
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226 | {
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227 | const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
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228 | eigen_assert(inner <= maxInnerIndex && "you try to acces a coeff that do not exist in the storage");
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229 | return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
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230 | }
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231 | }
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232 | }
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233 |
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234 | inline Scalar coeffDiag(Index idx) const {
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235 | eigen_assert(idx < outerSize());
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236 | eigen_assert(idx < innerSize());
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237 | return this->m_data.diag(idx);
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238 | }
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239 |
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240 | inline Scalar coeffLower(Index row, Index col) const {
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241 | const Index outer = IsRowMajor ? row : col;
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242 | const Index inner = IsRowMajor ? col : row;
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243 |
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244 | eigen_assert(outer < outerSize());
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245 | eigen_assert(inner < innerSize());
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246 | eigen_assert(inner != outer);
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247 |
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248 | if (IsRowMajor) {
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249 | const Index minInnerIndex = outer - m_data.lowerProfile(outer);
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250 | if (inner >= minInnerIndex)
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251 | return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
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252 | else
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253 | return Scalar(0);
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254 |
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255 | } else {
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256 | const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
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257 | if (inner <= maxInnerIndex)
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258 | return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
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259 | else
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260 | return Scalar(0);
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261 | }
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262 | }
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263 |
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264 | inline Scalar coeffUpper(Index row, Index col) const {
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265 | const Index outer = IsRowMajor ? row : col;
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266 | const Index inner = IsRowMajor ? col : row;
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267 |
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268 | eigen_assert(outer < outerSize());
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269 | eigen_assert(inner < innerSize());
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270 | eigen_assert(inner != outer);
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271 |
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272 | if (IsRowMajor) {
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273 | const Index minOuterIndex = inner - m_data.upperProfile(inner);
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274 | if (outer >= minOuterIndex)
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275 | return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
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276 | else
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277 | return Scalar(0);
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278 | } else {
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279 | const Index maxOuterIndex = inner + m_data.upperProfile(inner);
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280 | if (outer <= maxOuterIndex)
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281 | return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
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282 | else
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283 | return Scalar(0);
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284 | }
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285 | }
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286 |
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287 | inline Scalar& coeffRefDiag(Index idx) {
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288 | eigen_assert(idx < outerSize());
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289 | eigen_assert(idx < innerSize());
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290 | return this->m_data.diag(idx);
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291 | }
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292 |
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293 | inline Scalar& coeffRefLower(Index row, Index col) {
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294 | const Index outer = IsRowMajor ? row : col;
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295 | const Index inner = IsRowMajor ? col : row;
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296 |
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297 | eigen_assert(outer < outerSize());
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298 | eigen_assert(inner < innerSize());
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299 | eigen_assert(inner != outer);
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300 |
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301 | if (IsRowMajor) {
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302 | const Index minInnerIndex = outer - m_data.lowerProfile(outer);
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303 | eigen_assert(inner >= minInnerIndex && "you try to acces a coeff that do not exist in the storage");
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304 | return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
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305 | } else {
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306 | const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
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307 | eigen_assert(inner <= maxInnerIndex && "you try to acces a coeff that do not exist in the storage");
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308 | return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
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309 | }
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310 | }
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311 |
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312 | inline bool coeffExistLower(Index row, Index col) {
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313 | const Index outer = IsRowMajor ? row : col;
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314 | const Index inner = IsRowMajor ? col : row;
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315 |
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316 | eigen_assert(outer < outerSize());
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317 | eigen_assert(inner < innerSize());
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318 | eigen_assert(inner != outer);
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319 |
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320 | if (IsRowMajor) {
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321 | const Index minInnerIndex = outer - m_data.lowerProfile(outer);
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322 | return inner >= minInnerIndex;
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323 | } else {
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324 | const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
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325 | return inner <= maxInnerIndex;
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326 | }
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327 | }
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328 |
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329 | inline Scalar& coeffRefUpper(Index row, Index col) {
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330 | const Index outer = IsRowMajor ? row : col;
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331 | const Index inner = IsRowMajor ? col : row;
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332 |
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333 | eigen_assert(outer < outerSize());
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334 | eigen_assert(inner < innerSize());
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335 | eigen_assert(inner != outer);
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336 |
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337 | if (IsRowMajor) {
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338 | const Index minOuterIndex = inner - m_data.upperProfile(inner);
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339 | eigen_assert(outer >= minOuterIndex && "you try to acces a coeff that do not exist in the storage");
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340 | return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
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341 | } else {
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342 | const Index maxOuterIndex = inner + m_data.upperProfile(inner);
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343 | eigen_assert(outer <= maxOuterIndex && "you try to acces a coeff that do not exist in the storage");
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344 | return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
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345 | }
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346 | }
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347 |
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348 | inline bool coeffExistUpper(Index row, Index col) {
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349 | const Index outer = IsRowMajor ? row : col;
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350 | const Index inner = IsRowMajor ? col : row;
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351 |
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352 | eigen_assert(outer < outerSize());
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353 | eigen_assert(inner < innerSize());
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354 | eigen_assert(inner != outer);
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355 |
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356 | if (IsRowMajor) {
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357 | const Index minOuterIndex = inner - m_data.upperProfile(inner);
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358 | return outer >= minOuterIndex;
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359 | } else {
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360 | const Index maxOuterIndex = inner + m_data.upperProfile(inner);
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361 | return outer <= maxOuterIndex;
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362 | }
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363 | }
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364 |
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365 |
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366 | protected:
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367 |
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368 | public:
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369 | class InnerUpperIterator;
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370 | class InnerLowerIterator;
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371 |
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372 | class OuterUpperIterator;
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373 | class OuterLowerIterator;
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374 |
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375 | /** Removes all non zeros */
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376 | inline void setZero() {
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377 | m_data.clear();
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378 | memset(m_colStartIndex, 0, (m_outerSize + 1) * sizeof (Index));
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379 | memset(m_rowStartIndex, 0, (m_outerSize + 1) * sizeof (Index));
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380 | }
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381 |
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382 | /** \returns the number of non zero coefficients */
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383 | inline Index nonZeros() const {
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384 | return m_data.diagSize() + m_data.upperSize() + m_data.lowerSize();
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385 | }
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386 |
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387 | /** Preallocates \a reserveSize non zeros */
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388 | inline void reserve(Index reserveSize, Index reserveUpperSize, Index reserveLowerSize) {
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389 | m_data.reserve(reserveSize, reserveUpperSize, reserveLowerSize);
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390 | }
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391 |
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392 | /** \returns a reference to a novel non zero coefficient with coordinates \a row x \a col.
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393 |
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394 | *
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395 | * \warning This function can be extremely slow if the non zero coefficients
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396 | * are not inserted in a coherent order.
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397 | *
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398 | * After an insertion session, you should call the finalize() function.
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399 | */
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400 | EIGEN_DONT_INLINE Scalar & insert(Index row, Index col) {
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401 | const Index outer = IsRowMajor ? row : col;
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402 | const Index inner = IsRowMajor ? col : row;
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403 |
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404 | eigen_assert(outer < outerSize());
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405 | eigen_assert(inner < innerSize());
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406 |
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407 | if (outer == inner)
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408 | return m_data.diag(col);
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409 |
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410 | if (IsRowMajor) {
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411 | if (outer < inner) //upper matrix
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412 | {
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413 | Index minOuterIndex = 0;
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414 | minOuterIndex = inner - m_data.upperProfile(inner);
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415 |
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416 | if (outer < minOuterIndex) //The value does not yet exist
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417 | {
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418 | const Index previousProfile = m_data.upperProfile(inner);
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419 |
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420 | m_data.upperProfile(inner) = inner - outer;
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421 |
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422 |
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423 | const Index bandIncrement = m_data.upperProfile(inner) - previousProfile;
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424 | //shift data stored after this new one
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425 | const Index stop = m_colStartIndex[cols()];
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426 | const Index start = m_colStartIndex[inner];
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427 |
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428 |
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429 | for (Index innerIdx = stop; innerIdx >= start; innerIdx--) {
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430 | m_data.upper(innerIdx + bandIncrement) = m_data.upper(innerIdx);
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431 | }
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432 |
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433 | for (Index innerIdx = cols(); innerIdx > inner; innerIdx--) {
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434 | m_colStartIndex[innerIdx] += bandIncrement;
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435 | }
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436 |
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437 | //zeros new data
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438 | memset(this->_upperPtr() + start, 0, (bandIncrement - 1) * sizeof (Scalar));
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439 |
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440 | return m_data.upper(m_colStartIndex[inner]);
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441 | } else {
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442 | return m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
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443 | }
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444 | }
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445 |
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446 | if (outer > inner) //lower matrix
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447 | {
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448 | const Index minInnerIndex = outer - m_data.lowerProfile(outer);
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449 | if (inner < minInnerIndex) //The value does not yet exist
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450 | {
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451 | const Index previousProfile = m_data.lowerProfile(outer);
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452 | m_data.lowerProfile(outer) = outer - inner;
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453 |
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454 | const Index bandIncrement = m_data.lowerProfile(outer) - previousProfile;
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455 | //shift data stored after this new one
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456 | const Index stop = m_rowStartIndex[rows()];
|
---|
457 | const Index start = m_rowStartIndex[outer];
|
---|
458 |
|
---|
459 |
|
---|
460 | for (Index innerIdx = stop; innerIdx >= start; innerIdx--) {
|
---|
461 | m_data.lower(innerIdx + bandIncrement) = m_data.lower(innerIdx);
|
---|
462 | }
|
---|
463 |
|
---|
464 | for (Index innerIdx = rows(); innerIdx > outer; innerIdx--) {
|
---|
465 | m_rowStartIndex[innerIdx] += bandIncrement;
|
---|
466 | }
|
---|
467 |
|
---|
468 | //zeros new data
|
---|
469 | memset(this->_lowerPtr() + start, 0, (bandIncrement - 1) * sizeof (Scalar));
|
---|
470 | return m_data.lower(m_rowStartIndex[outer]);
|
---|
471 | } else {
|
---|
472 | return m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
|
---|
473 | }
|
---|
474 | }
|
---|
475 | } else {
|
---|
476 | if (outer > inner) //upper matrix
|
---|
477 | {
|
---|
478 | const Index maxOuterIndex = inner + m_data.upperProfile(inner);
|
---|
479 | if (outer > maxOuterIndex) //The value does not yet exist
|
---|
480 | {
|
---|
481 | const Index previousProfile = m_data.upperProfile(inner);
|
---|
482 | m_data.upperProfile(inner) = outer - inner;
|
---|
483 |
|
---|
484 | const Index bandIncrement = m_data.upperProfile(inner) - previousProfile;
|
---|
485 | //shift data stored after this new one
|
---|
486 | const Index stop = m_rowStartIndex[rows()];
|
---|
487 | const Index start = m_rowStartIndex[inner + 1];
|
---|
488 |
|
---|
489 | for (Index innerIdx = stop; innerIdx >= start; innerIdx--) {
|
---|
490 | m_data.upper(innerIdx + bandIncrement) = m_data.upper(innerIdx);
|
---|
491 | }
|
---|
492 |
|
---|
493 | for (Index innerIdx = inner + 1; innerIdx < outerSize() + 1; innerIdx++) {
|
---|
494 | m_rowStartIndex[innerIdx] += bandIncrement;
|
---|
495 | }
|
---|
496 | memset(this->_upperPtr() + m_rowStartIndex[inner] + previousProfile + 1, 0, (bandIncrement - 1) * sizeof (Scalar));
|
---|
497 | return m_data.upper(m_rowStartIndex[inner] + m_data.upperProfile(inner));
|
---|
498 | } else {
|
---|
499 | return m_data.upper(m_rowStartIndex[inner] + (outer - inner));
|
---|
500 | }
|
---|
501 | }
|
---|
502 |
|
---|
503 | if (outer < inner) //lower matrix
|
---|
504 | {
|
---|
505 | const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
|
---|
506 | if (inner > maxInnerIndex) //The value does not yet exist
|
---|
507 | {
|
---|
508 | const Index previousProfile = m_data.lowerProfile(outer);
|
---|
509 | m_data.lowerProfile(outer) = inner - outer;
|
---|
510 |
|
---|
511 | const Index bandIncrement = m_data.lowerProfile(outer) - previousProfile;
|
---|
512 | //shift data stored after this new one
|
---|
513 | const Index stop = m_colStartIndex[cols()];
|
---|
514 | const Index start = m_colStartIndex[outer + 1];
|
---|
515 |
|
---|
516 | for (Index innerIdx = stop; innerIdx >= start; innerIdx--) {
|
---|
517 | m_data.lower(innerIdx + bandIncrement) = m_data.lower(innerIdx);
|
---|
518 | }
|
---|
519 |
|
---|
520 | for (Index innerIdx = outer + 1; innerIdx < outerSize() + 1; innerIdx++) {
|
---|
521 | m_colStartIndex[innerIdx] += bandIncrement;
|
---|
522 | }
|
---|
523 | memset(this->_lowerPtr() + m_colStartIndex[outer] + previousProfile + 1, 0, (bandIncrement - 1) * sizeof (Scalar));
|
---|
524 | return m_data.lower(m_colStartIndex[outer] + m_data.lowerProfile(outer));
|
---|
525 | } else {
|
---|
526 | return m_data.lower(m_colStartIndex[outer] + (inner - outer));
|
---|
527 | }
|
---|
528 | }
|
---|
529 | }
|
---|
530 | }
|
---|
531 |
|
---|
532 | /** Must be called after inserting a set of non zero entries.
|
---|
533 | */
|
---|
534 | inline void finalize() {
|
---|
535 | if (IsRowMajor) {
|
---|
536 | if (rows() > cols())
|
---|
537 | m_data.resize(cols(), cols(), rows(), m_colStartIndex[cols()] + 1, m_rowStartIndex[rows()] + 1);
|
---|
538 | else
|
---|
539 | m_data.resize(rows(), cols(), rows(), m_colStartIndex[cols()] + 1, m_rowStartIndex[rows()] + 1);
|
---|
540 |
|
---|
541 | // eigen_assert(rows() == cols() && "memory reorganisatrion only works with suare matrix");
|
---|
542 | //
|
---|
543 | // Scalar* newArray = new Scalar[m_colStartIndex[cols()] + 1 + m_rowStartIndex[rows()] + 1];
|
---|
544 | // Index dataIdx = 0;
|
---|
545 | // for (Index row = 0; row < rows(); row++) {
|
---|
546 | //
|
---|
547 | // const Index nbLowerElts = m_rowStartIndex[row + 1] - m_rowStartIndex[row];
|
---|
548 | // // std::cout << "nbLowerElts" << nbLowerElts << std::endl;
|
---|
549 | // memcpy(newArray + dataIdx, m_data.m_lower + m_rowStartIndex[row], nbLowerElts * sizeof (Scalar));
|
---|
550 | // m_rowStartIndex[row] = dataIdx;
|
---|
551 | // dataIdx += nbLowerElts;
|
---|
552 | //
|
---|
553 | // const Index nbUpperElts = m_colStartIndex[row + 1] - m_colStartIndex[row];
|
---|
554 | // memcpy(newArray + dataIdx, m_data.m_upper + m_colStartIndex[row], nbUpperElts * sizeof (Scalar));
|
---|
555 | // m_colStartIndex[row] = dataIdx;
|
---|
556 | // dataIdx += nbUpperElts;
|
---|
557 | //
|
---|
558 | //
|
---|
559 | // }
|
---|
560 | // //todo : don't access m_data profile directly : add an accessor from SkylineMatrix
|
---|
561 | // m_rowStartIndex[rows()] = m_rowStartIndex[rows()-1] + m_data.lowerProfile(rows()-1);
|
---|
562 | // m_colStartIndex[cols()] = m_colStartIndex[cols()-1] + m_data.upperProfile(cols()-1);
|
---|
563 | //
|
---|
564 | // delete[] m_data.m_lower;
|
---|
565 | // delete[] m_data.m_upper;
|
---|
566 | //
|
---|
567 | // m_data.m_lower = newArray;
|
---|
568 | // m_data.m_upper = newArray;
|
---|
569 | } else {
|
---|
570 | if (rows() > cols())
|
---|
571 | m_data.resize(cols(), rows(), cols(), m_rowStartIndex[cols()] + 1, m_colStartIndex[cols()] + 1);
|
---|
572 | else
|
---|
573 | m_data.resize(rows(), rows(), cols(), m_rowStartIndex[rows()] + 1, m_colStartIndex[rows()] + 1);
|
---|
574 | }
|
---|
575 | }
|
---|
576 |
|
---|
577 | inline void squeeze() {
|
---|
578 | finalize();
|
---|
579 | m_data.squeeze();
|
---|
580 | }
|
---|
581 |
|
---|
582 | void prune(Scalar reference, RealScalar epsilon = dummy_precision<RealScalar > ()) {
|
---|
583 | //TODO
|
---|
584 | }
|
---|
585 |
|
---|
586 | /** Resizes the matrix to a \a rows x \a cols matrix and initializes it to zero
|
---|
587 | * \sa resizeNonZeros(Index), reserve(), setZero()
|
---|
588 | */
|
---|
589 | void resize(size_t rows, size_t cols) {
|
---|
590 | const Index diagSize = rows > cols ? cols : rows;
|
---|
591 | m_innerSize = IsRowMajor ? cols : rows;
|
---|
592 |
|
---|
593 | eigen_assert(rows == cols && "Skyline matrix must be square matrix");
|
---|
594 |
|
---|
595 | if (diagSize % 2) { // diagSize is odd
|
---|
596 | const Index k = (diagSize - 1) / 2;
|
---|
597 |
|
---|
598 | m_data.resize(diagSize, IsRowMajor ? cols : rows, IsRowMajor ? rows : cols,
|
---|
599 | 2 * k * k + k + 1,
|
---|
600 | 2 * k * k + k + 1);
|
---|
601 |
|
---|
602 | } else // diagSize is even
|
---|
603 | {
|
---|
604 | const Index k = diagSize / 2;
|
---|
605 | m_data.resize(diagSize, IsRowMajor ? cols : rows, IsRowMajor ? rows : cols,
|
---|
606 | 2 * k * k - k + 1,
|
---|
607 | 2 * k * k - k + 1);
|
---|
608 | }
|
---|
609 |
|
---|
610 | if (m_colStartIndex && m_rowStartIndex) {
|
---|
611 | delete[] m_colStartIndex;
|
---|
612 | delete[] m_rowStartIndex;
|
---|
613 | }
|
---|
614 | m_colStartIndex = new Index [cols + 1];
|
---|
615 | m_rowStartIndex = new Index [rows + 1];
|
---|
616 | m_outerSize = diagSize;
|
---|
617 |
|
---|
618 | m_data.reset();
|
---|
619 | m_data.clear();
|
---|
620 |
|
---|
621 | m_outerSize = diagSize;
|
---|
622 | memset(m_colStartIndex, 0, (cols + 1) * sizeof (Index));
|
---|
623 | memset(m_rowStartIndex, 0, (rows + 1) * sizeof (Index));
|
---|
624 | }
|
---|
625 |
|
---|
626 | void resizeNonZeros(Index size) {
|
---|
627 | m_data.resize(size);
|
---|
628 | }
|
---|
629 |
|
---|
630 | inline SkylineMatrix()
|
---|
631 | : m_outerSize(-1), m_innerSize(0), m_colStartIndex(0), m_rowStartIndex(0) {
|
---|
632 | resize(0, 0);
|
---|
633 | }
|
---|
634 |
|
---|
635 | inline SkylineMatrix(size_t rows, size_t cols)
|
---|
636 | : m_outerSize(0), m_innerSize(0), m_colStartIndex(0), m_rowStartIndex(0) {
|
---|
637 | resize(rows, cols);
|
---|
638 | }
|
---|
639 |
|
---|
640 | template<typename OtherDerived>
|
---|
641 | inline SkylineMatrix(const SkylineMatrixBase<OtherDerived>& other)
|
---|
642 | : m_outerSize(0), m_innerSize(0), m_colStartIndex(0), m_rowStartIndex(0) {
|
---|
643 | *this = other.derived();
|
---|
644 | }
|
---|
645 |
|
---|
646 | inline SkylineMatrix(const SkylineMatrix & other)
|
---|
647 | : Base(), m_outerSize(0), m_innerSize(0), m_colStartIndex(0), m_rowStartIndex(0) {
|
---|
648 | *this = other.derived();
|
---|
649 | }
|
---|
650 |
|
---|
651 | inline void swap(SkylineMatrix & other) {
|
---|
652 | //EIGEN_DBG_SKYLINE(std::cout << "SkylineMatrix:: swap\n");
|
---|
653 | std::swap(m_colStartIndex, other.m_colStartIndex);
|
---|
654 | std::swap(m_rowStartIndex, other.m_rowStartIndex);
|
---|
655 | std::swap(m_innerSize, other.m_innerSize);
|
---|
656 | std::swap(m_outerSize, other.m_outerSize);
|
---|
657 | m_data.swap(other.m_data);
|
---|
658 | }
|
---|
659 |
|
---|
660 | inline SkylineMatrix & operator=(const SkylineMatrix & other) {
|
---|
661 | std::cout << "SkylineMatrix& operator=(const SkylineMatrix& other)\n";
|
---|
662 | if (other.isRValue()) {
|
---|
663 | swap(other.const_cast_derived());
|
---|
664 | } else {
|
---|
665 | resize(other.rows(), other.cols());
|
---|
666 | memcpy(m_colStartIndex, other.m_colStartIndex, (m_outerSize + 1) * sizeof (Index));
|
---|
667 | memcpy(m_rowStartIndex, other.m_rowStartIndex, (m_outerSize + 1) * sizeof (Index));
|
---|
668 | m_data = other.m_data;
|
---|
669 | }
|
---|
670 | return *this;
|
---|
671 | }
|
---|
672 |
|
---|
673 | template<typename OtherDerived>
|
---|
674 | inline SkylineMatrix & operator=(const SkylineMatrixBase<OtherDerived>& other) {
|
---|
675 | const bool needToTranspose = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit);
|
---|
676 | if (needToTranspose) {
|
---|
677 | // TODO
|
---|
678 | // return *this;
|
---|
679 | } else {
|
---|
680 | // there is no special optimization
|
---|
681 | return SkylineMatrixBase<SkylineMatrix>::operator=(other.derived());
|
---|
682 | }
|
---|
683 | }
|
---|
684 |
|
---|
685 | friend std::ostream & operator <<(std::ostream & s, const SkylineMatrix & m) {
|
---|
686 |
|
---|
687 | EIGEN_DBG_SKYLINE(
|
---|
688 | std::cout << "upper elements : " << std::endl;
|
---|
689 | for (Index i = 0; i < m.m_data.upperSize(); i++)
|
---|
690 | std::cout << m.m_data.upper(i) << "\t";
|
---|
691 | std::cout << std::endl;
|
---|
692 | std::cout << "upper profile : " << std::endl;
|
---|
693 | for (Index i = 0; i < m.m_data.upperProfileSize(); i++)
|
---|
694 | std::cout << m.m_data.upperProfile(i) << "\t";
|
---|
695 | std::cout << std::endl;
|
---|
696 | std::cout << "lower startIdx : " << std::endl;
|
---|
697 | for (Index i = 0; i < m.m_data.upperProfileSize(); i++)
|
---|
698 | std::cout << (IsRowMajor ? m.m_colStartIndex[i] : m.m_rowStartIndex[i]) << "\t";
|
---|
699 | std::cout << std::endl;
|
---|
700 |
|
---|
701 |
|
---|
702 | std::cout << "lower elements : " << std::endl;
|
---|
703 | for (Index i = 0; i < m.m_data.lowerSize(); i++)
|
---|
704 | std::cout << m.m_data.lower(i) << "\t";
|
---|
705 | std::cout << std::endl;
|
---|
706 | std::cout << "lower profile : " << std::endl;
|
---|
707 | for (Index i = 0; i < m.m_data.lowerProfileSize(); i++)
|
---|
708 | std::cout << m.m_data.lowerProfile(i) << "\t";
|
---|
709 | std::cout << std::endl;
|
---|
710 | std::cout << "lower startIdx : " << std::endl;
|
---|
711 | for (Index i = 0; i < m.m_data.lowerProfileSize(); i++)
|
---|
712 | std::cout << (IsRowMajor ? m.m_rowStartIndex[i] : m.m_colStartIndex[i]) << "\t";
|
---|
713 | std::cout << std::endl;
|
---|
714 | );
|
---|
715 | for (Index rowIdx = 0; rowIdx < m.rows(); rowIdx++) {
|
---|
716 | for (Index colIdx = 0; colIdx < m.cols(); colIdx++) {
|
---|
717 | s << m.coeff(rowIdx, colIdx) << "\t";
|
---|
718 | }
|
---|
719 | s << std::endl;
|
---|
720 | }
|
---|
721 | return s;
|
---|
722 | }
|
---|
723 |
|
---|
724 | /** Destructor */
|
---|
725 | inline ~SkylineMatrix() {
|
---|
726 | delete[] m_colStartIndex;
|
---|
727 | delete[] m_rowStartIndex;
|
---|
728 | }
|
---|
729 |
|
---|
730 | /** Overloaded for performance */
|
---|
731 | Scalar sum() const;
|
---|
732 | };
|
---|
733 |
|
---|
734 | template<typename Scalar, int _Options>
|
---|
735 | class SkylineMatrix<Scalar, _Options>::InnerUpperIterator {
|
---|
736 | public:
|
---|
737 |
|
---|
738 | InnerUpperIterator(const SkylineMatrix& mat, Index outer)
|
---|
739 | : m_matrix(mat), m_outer(outer),
|
---|
740 | m_id(_Options == RowMajor ? mat.m_colStartIndex[outer] : mat.m_rowStartIndex[outer] + 1),
|
---|
741 | m_start(m_id),
|
---|
742 | m_end(_Options == RowMajor ? mat.m_colStartIndex[outer + 1] : mat.m_rowStartIndex[outer + 1] + 1) {
|
---|
743 | }
|
---|
744 |
|
---|
745 | inline InnerUpperIterator & operator++() {
|
---|
746 | m_id++;
|
---|
747 | return *this;
|
---|
748 | }
|
---|
749 |
|
---|
750 | inline InnerUpperIterator & operator+=(Index shift) {
|
---|
751 | m_id += shift;
|
---|
752 | return *this;
|
---|
753 | }
|
---|
754 |
|
---|
755 | inline Scalar value() const {
|
---|
756 | return m_matrix.m_data.upper(m_id);
|
---|
757 | }
|
---|
758 |
|
---|
759 | inline Scalar* valuePtr() {
|
---|
760 | return const_cast<Scalar*> (&(m_matrix.m_data.upper(m_id)));
|
---|
761 | }
|
---|
762 |
|
---|
763 | inline Scalar& valueRef() {
|
---|
764 | return const_cast<Scalar&> (m_matrix.m_data.upper(m_id));
|
---|
765 | }
|
---|
766 |
|
---|
767 | inline Index index() const {
|
---|
768 | return IsRowMajor ? m_outer - m_matrix.m_data.upperProfile(m_outer) + (m_id - m_start) :
|
---|
769 | m_outer + (m_id - m_start) + 1;
|
---|
770 | }
|
---|
771 |
|
---|
772 | inline Index row() const {
|
---|
773 | return IsRowMajor ? index() : m_outer;
|
---|
774 | }
|
---|
775 |
|
---|
776 | inline Index col() const {
|
---|
777 | return IsRowMajor ? m_outer : index();
|
---|
778 | }
|
---|
779 |
|
---|
780 | inline size_t size() const {
|
---|
781 | return m_matrix.m_data.upperProfile(m_outer);
|
---|
782 | }
|
---|
783 |
|
---|
784 | inline operator bool() const {
|
---|
785 | return (m_id < m_end) && (m_id >= m_start);
|
---|
786 | }
|
---|
787 |
|
---|
788 | protected:
|
---|
789 | const SkylineMatrix& m_matrix;
|
---|
790 | const Index m_outer;
|
---|
791 | Index m_id;
|
---|
792 | const Index m_start;
|
---|
793 | const Index m_end;
|
---|
794 | };
|
---|
795 |
|
---|
796 | template<typename Scalar, int _Options>
|
---|
797 | class SkylineMatrix<Scalar, _Options>::InnerLowerIterator {
|
---|
798 | public:
|
---|
799 |
|
---|
800 | InnerLowerIterator(const SkylineMatrix& mat, Index outer)
|
---|
801 | : m_matrix(mat),
|
---|
802 | m_outer(outer),
|
---|
803 | m_id(_Options == RowMajor ? mat.m_rowStartIndex[outer] : mat.m_colStartIndex[outer] + 1),
|
---|
804 | m_start(m_id),
|
---|
805 | m_end(_Options == RowMajor ? mat.m_rowStartIndex[outer + 1] : mat.m_colStartIndex[outer + 1] + 1) {
|
---|
806 | }
|
---|
807 |
|
---|
808 | inline InnerLowerIterator & operator++() {
|
---|
809 | m_id++;
|
---|
810 | return *this;
|
---|
811 | }
|
---|
812 |
|
---|
813 | inline InnerLowerIterator & operator+=(Index shift) {
|
---|
814 | m_id += shift;
|
---|
815 | return *this;
|
---|
816 | }
|
---|
817 |
|
---|
818 | inline Scalar value() const {
|
---|
819 | return m_matrix.m_data.lower(m_id);
|
---|
820 | }
|
---|
821 |
|
---|
822 | inline Scalar* valuePtr() {
|
---|
823 | return const_cast<Scalar*> (&(m_matrix.m_data.lower(m_id)));
|
---|
824 | }
|
---|
825 |
|
---|
826 | inline Scalar& valueRef() {
|
---|
827 | return const_cast<Scalar&> (m_matrix.m_data.lower(m_id));
|
---|
828 | }
|
---|
829 |
|
---|
830 | inline Index index() const {
|
---|
831 | return IsRowMajor ? m_outer - m_matrix.m_data.lowerProfile(m_outer) + (m_id - m_start) :
|
---|
832 | m_outer + (m_id - m_start) + 1;
|
---|
833 | ;
|
---|
834 | }
|
---|
835 |
|
---|
836 | inline Index row() const {
|
---|
837 | return IsRowMajor ? m_outer : index();
|
---|
838 | }
|
---|
839 |
|
---|
840 | inline Index col() const {
|
---|
841 | return IsRowMajor ? index() : m_outer;
|
---|
842 | }
|
---|
843 |
|
---|
844 | inline size_t size() const {
|
---|
845 | return m_matrix.m_data.lowerProfile(m_outer);
|
---|
846 | }
|
---|
847 |
|
---|
848 | inline operator bool() const {
|
---|
849 | return (m_id < m_end) && (m_id >= m_start);
|
---|
850 | }
|
---|
851 |
|
---|
852 | protected:
|
---|
853 | const SkylineMatrix& m_matrix;
|
---|
854 | const Index m_outer;
|
---|
855 | Index m_id;
|
---|
856 | const Index m_start;
|
---|
857 | const Index m_end;
|
---|
858 | };
|
---|
859 |
|
---|
860 | } // end namespace Eigen
|
---|
861 |
|
---|
862 | #endif // EIGEN_SkylineMatrix_H
|
---|